As well known, naturally occurring enzymes are exceptional catalysts that recruit their substrates to a precisely defined active site with precisely aligned catalytic amino acid residues and an ideal protein environment for rapid chemical transformation and, thus, accelerate chemical reactions under mild conditions. For example, acetylcholinesterase (AChE) has been well known as the most efficient hydrolytic enzyme, with the catalytic activity for neurotransmitter acetylcholine (ACh) approaching the limit allowed by the diffusion of the substrate. It is highly desired for drug discovery, drug delivery, therapeutic development, chemical engineering, natural product production, chemical defense, green chemistry, and biomediation of xenobiotic pollutants to have the ability to design new enzymes with the catalytic activities higher than or comparable to those of naturally occurring enzymes. Recently reported studies have demonstrated that computational enzyme design can lead to discovery of active enzymes. However, computationally designed enzymes to date are much less efficient, with the catalytic activities being more than six orders of magnitude from the diffusion limit. The low activities of computationally designed enzymes and the overall low design success rate are due to the limitation of popularly used computational design methods without a sophisticated approach to prediction of the catalytic activity. Here we describe the use of a more sophisticated computational enzyme design approach to create a new, extraordinarily efficient enzyme with the catalytic efficiency specific for cocaine detoxification comparable to or even higher than that of an evolutionarily perfect enzyme, i.e. AChE (for ACh) known as the most efficient naturally occurring esterase.

Computational design of a highly efficient enzyme

246th National Meeting (2013)

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